Top AI & Machine Learning Jobs

Designs and ships production multi-agent AI systems for KYB, underwriting, case review, and risk monitoring. Owns agent architecture, retrieval, tool integrations, evaluations, observability, MLOps, reliability, and compliance guardrails. Requires strong Python engineering, agent framework expertise, distributed systems experience, Kubernetes operations, and production LLM deployment skills. Mentors engineers and partners with product, security, compliance, and applied science teams. Remote employees travel to Orlando at least twice annually.

Build and operate production AI agents, agentic workflows, MCP servers, and CX applications. Develop Python and TypeScript services, AWS infrastructure, integrations, testing, observability, security controls, and CI/CD pipelines. Partner with Customer Success and other CX teams to prototype solutions, translate workflows into technical plans, and deliver reusable platforms. Mentor engineers and non-engineers, document systems, establish governance, and support reliable handoff and shared ownership.

Build scalable AI and machine learning platform capabilities across model development, deployment, monitoring, cataloging, tagging, and LLM hosting. Collaborate with customers and cross-functional teams to integrate platforms such as AWS SageMaker and Azure ML. Develop secure APIs and distributed backend systems, optimize cloud and containerized services, and establish robust testing and CI/CD pipelines.

Lead research, development, deployment, and measurement of machine learning models for forecasting, pricing, inventory optimization, personalization, and generative AI. Build production-grade software and scalable machine learning workflows, collaborate with engineering and business teams, communicate insights to stakeholders, and support enterprise data science applications across testing and production environments.

Design, implement, and maintain production-oriented NLP and LLM models; build data pipelines and HPC analyses; integrate ML solutions with clinical research tools; ensure data quality, privacy compliance, and collaborate across multidisciplinary teams.

Own the infrastructure, deployment, monitoring, versioning, and rollback systems that move computer vision and machine learning models from development into reliable cloud and edge production environments. Process and optimize pipelines handling 11 million daily images, support roadside hardware deployments, detect model degradation, and manage updates and retraining. The role requires strong production software engineering skills, adaptability, and interest in operationalizing ML at scale.

Lead research and engineering of production-grade ML/AI models (NLP, generative and agentic systems). Design, evaluate, and deploy scalable models, implement evaluation-driven development, mentor junior scientists, collaborate with product and engineering, and embed observability and CI/CD benchmarking to ensure reliable, ethical, and high-impact AI features.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.